脉冲噪声环境下基于分数低阶循环相关的自适应时延估计方法

Novel adaptive time delay estimation method based on the fractional lower order cyclic correlation in impulsive noise environment

以α稳定分布作为噪声模型,研究了非高斯噪声对传统的二阶循环统计量的影响,提出了分数低阶循环相关的概念,研究并证明了其性质,对传统意义上的二阶循环统计量进行了广义化,并在此基础上结合自适应技术提出了一种基于分数低阶循环相关的自适应时延估计方法。计算机模拟表明,该方法可有效估计高斯噪声和脉冲噪声条件下的时变和非时变时延值,其性能不仅优于基于二阶循环相关的自适应时延估计算法,而且优于最小平均p范数(LMP)自适应时延估计方法。

α-stable distribution was taken as the noise model. Considering the effect of o-stable distributed noises for the classical second order cyclic statistics, a novel fractional lower order cyclic statistic which was the generalization of the second order one. Based on the proposed concept, a novel adaptive time delay estimation method was developed. Simulations show that the proposed algorithm gets accurate estimation for both time-varying and time invariant time delays under Gaussian and impulsive noises conditions. Simulations also demonstrate that the performance of the proposed algorithm is superior to both LMP （least mean p-norm） and adaptive time delay estimation methods in α -stable distributed noises.